United States

Prior research has shown that people vary considerably in how they interpret political concepts, a variability often attributed to liberal–conservative differences underlying political polarization. In this study, rather than focusing on the liberal–conservative dichotomy, we considered personality and morality variables as possible predictors of cognitive diversity in subjects’ interpretation of political concepts. Participants completed brief personality (HEXACO) and morality (MAC) assessments, followed by a series of association ratings for the concepts of freedom, justice, and authority. We found that certain personality traits and moral dimensions correlate with higher associations between probe concepts. Furthermore, clustering of political inclination on morality dimensions and concept ratings suggested that the latter made a limited contribution to political diversity, only raising the number of clusters from 2 to 3.
Keywords: personality; morality; conceptual diversity; freedom; justice; authority

CogSci 2025

Exploring the Cognitive Diversity of Political Concepts

concepts and categories

social cognition

statistics

psychology

representation

survey

poster

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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#### About

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
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* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
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* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
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#### Code of Conduct

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
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The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

People rarely face the same problem twice, but many problems are similar. What strategies do people discover when solving similar problems, and what is the impact of that experience on how they approach new ones? Here we investigated how people’s strategies changed over time while solving sets of related visual reasoning problems. Participants (N=42) were given a sequence of “tangram” puzzles to solve, which could be reconstructed either exclusively with small pieces or with a special large piece to reconstruct the tangram in fewer moves. Later, participants attempted puzzles where a different large piece was helpful instead, so we could measure the impact of prior experience on how they approached puzzles favoring a different strategy. We found that participants reconstructed tangrams more quickly over time, and generally used the appropriate large piece for the problem at hand, reflecting their ability to flexibly adapt their strategies to new problems.

Consequences of prior experience on visual problem solving

Term series problems have been used to study deductive reasoning based on propositions by asking for the order relations of given terms. Some tasks, known as indeterminate term series (ITS), leave certain order relations unspecified. This study explored computational models of how people solve ITS, examining the mental models they form and the way they derive answers. We tested two strategies: a total order strategy, which constructs all satisfying total order mental models, and a symmetry strategy, which collapses interchangeable terms into a total-order-like symmetric model. Statistical analyses of experimental data suggest that participants used total order strategies more often for easier ITS problems and symmetry strategies more for harder ones. This finding supports the hypothesis that encoding the order relations by symmetric structures can reduce cognitive load when solving ITS problems.

Cognitive Strategies in Solving Indeterminate Term Series Problems: A Comparison with Total Order and Symmetric Partial Order Strategies

The letter fluency task is the timed listing of words that begin with a specific letter (e.g., words starting with T). Participants often list words in phonologically related clusters (e.g., tank, task, tap) and occasionally switch clusters (e.g., tap, thud). This process has been likened to patch switching in animal foraging. Optimal performance requires switching clusters in a manner that maximizes the rate of retrieving words, known as the marginal value theorem. Previous work has found evidence for this in semantic fluency. The current study tests whether people adhere to the marginal value theorem in letter fluency and whether executive functioning is associated with optimal performance. Three letter cues (T, N, and J) and one semantic cue (animals) were administered. Results are consistent with optimal search in N and J, but not T or animals. These findings provide mixed support that people search optimally during letter fluency.

Foraging Connections: Optimal Foraging in Letter Fluency

Inferencing is essential for reading comprehension and higher-order thinking (Kendeou et al., 2019). Teachers are encouraged to teach students inferential language (Foorman et al., 2016), but the role of connected text itself in scaffolding inferencing remains unclear. In this study, we examined if texts used in a high-impact tutoring program for elementary students vary in inferential language. We identified four categories of inferential language: mental state terms, emotional state terms, metacognitive knowledge, and cognition and active processing. We used an AI-driven text analysis to tally these in the nine most popular text sources. Findings revealed minimal inferential language overall, with significant variation across sources, p < 0.001. As text difficulty increased, mental and emotional state terms (e.g., think, feel, happy) became more common, but academic and metacognitive terms (e.g., realize, reflect, analyze) remained scarce. Results highlight the need for further research on how text complexity influences student outcomes.

Inferential Language is Limited and Unevenly Distributed in Popular Texts Used For Literacy Tutoring

Explicit strategies play an important role in visuomotor adaptation, but are subject to substantial capacity limits, calling into question their efficacy in complex settings. For instance, recent work has shown that when task complexity exceeds working memory capacity, observers are no longer able to fully adapt. Here, using a visuomotor rotation task in which participants were tasked with simultaneously adapting to eight target-rotation pairs, we examined the extent to which such capacity constraints may be ameliorated by reward-based feedback. We found that the mere presence of explicit reward did not change participants’ behavior – a uniform reward distribution led to a similar pattern of behavior as has been previously reported. However, when a subset of target-rotation pairs was associated with a greater reward magnitude, participants demonstrated enhanced adaptation to them, which improved overall performance. These findings highlight the utility of reinforcement learning for enabling motor learning and adaptation in complex tasks.

Influence of reward on visuomotor adaptation in complex tasks

In cognitive neuroscience, there has been growing interest in adopting sequential sampling models (SSM) as the generative choice function for reinforcement learning (RLSSM) to jointly account for decision dynamics within and across trials. However, such approaches have been limited by computational tractability due to lack of closed-form likelihoods for the decision process or expensive trial-by-trial evaluation of complex reinforcement learning (RL) processes. We enable hierarchical Bayesian estimation for a broad class of RLSSM models, using Likelihood Approximation Networks (LANs) in conjunction with differentiable RL likelihoods to leverage fast gradient-based inference methods including Hamiltonian Monte Carlo or Variational Inference (VI). To showcase the scalability and faster convergence with our approach, we consider the Reinforcement Learning - Working Memory (RLWM) task and model with multiple interacting generative learning processes. We show that our method enables accurate recovery of the posterior parameter distributions in arbitrarily complex RLSSM paradigms, and moreover, that in comparison, fitting data with the equivalent choice-only model yields a biased estimator of the true generative process. Moreover, leveraging the SSM with efficient inference allows us to uncover a heretofore undescribed cognitive process within the RLWM task, whereby participants proactively adjust the decision threshold as a function of WM load.

Fast and robust Bayesian inference for modular combinations of dynamic learning and decision models

When presented with a yes-no question, humans tend to say 'yes’ regardless of the ground truth. This 'yes-bias' can be attributed either to the social pressure to agree with an interlocutor or simply to the tendency to mimic the distribution of the input data. Here, we estimate 'yes-no’ response bias in language models (LMs), with the goal of distinguishing the two theories, and explore two strategies for bias correction. We develop two yes-no question datasets derived from existing world knowledge datasets, and test 16 open-weight LMs. We find that LMs often show response bias on yes-no questions, but that it is highly variable, deviating from bias observed in humans. We further present a novel bias correction method, which eliminates bias and improves model performance. Evidence of non-humanlike response bias in LMs informs us on the source of yes-bias in humans, and the efficacy of our bias correction method holds promise for LM evaluation.

Estimating and Correcting Yes-No Bias in Language Models

In order to fulfill goals, humans make use of cognitive control, which is a suite of processes to plan and manage thoughts and actions. One such process is response inhibition, which entails stopping a response when an action becomes inappropriate. Traditionally, response inhibition is measured in experimental settings in which humans have unilateral responsibility for inhibiting the action. However, in the real world, humans are increasingly sharing control with artificial intelligence (AI), with the paradigmatic case being partially automated vehicles. We designed an experiment that includes some aspects of partially automated vehicles and found that when humans share control with an AI that often but does not always stop, human response inhibition is significantly slowed even when the AI does not intervene. This reveals a cost of sharing control to human cognitive control, suggesting that the benefits of partial automation should be weighed against the costs of impaired human control.

Shared control impairs cognitive control: Human responses inhibition slows when machines fail to inhibit 

Many languages mark either accusative case (for objects of transitives) or ergative case (for subjects of transitives), but some `split ergative' languages mix the two systems depending on the type of nominal. It has been noted that these languages tend towards marking the less frequent case for each nominal type. This raises the question of what mechanism could underlie the emergence of such an efficient system. We propose a model that can provide an explanation, based on a simple reinforcement learning framework and simple assumptions about asymmetries between the kinds of nominals (e.g., pronouns vs. full noun phrases) that appear in subject vs. object position.

Reinforcement learning produces efficient case-marking systems

Learning language requires learning not only the content of language, but also how to use language to communicate. Iterated reference games provide a window into such skills, requiring rich communication as participants converge on mutually understandable names for initially novel referents. Some early experiments are interpreted as evidence that 4-5-year-old children cannot converge to the mutually understandable names needed to communicate in an iterated reference game. Here, we revisit young children's referential communicative abilities using a simpler, child-friendly paradigm. Across 51 pairs of children, we found that 4-5-year-olds successfully established reference with each other. Children were 85% accurate, and they often used descriptions similar to their partner's. These findings suggest that children’s capacity to construct effective referring expressions in novel contexts emerges earlier than once thought, consistent with the view that children show early pragmatic competence in supportive contexts.

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Consequences of prior experience on visual problem solving

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Consequences of prior experience on visual problem solving

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